Clustering and Unsupervised Pattern Discovery
Unsupervised learning methods reveal structure in unlabeled datasets. This investigates clustering algorithms such as k-means clustering, DBSCAN, and hierarchical agglomerative clustering. Emphasis is placed on determining grouping quality using measures like the silhouette score. Learners examine similarity through distance metrics and feature relationships. Applications include market segmentation and behavioral analysis. It fosters independent pattern discovery skills.
Unsupervised Learning Focus:
- Clustering algorithms and criteria
- Similarity measures and distance metrics
- Pattern identification without labels
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